Nonlinear model with random inflection points for modeling neurodegenerative disease progression
Nonlinear model with random inflection points for modeling neurodegenerative disease progression
复制标题
用于模拟神经退行性疾病进展的具有随机拐点的非线性模型
DOI：
10.1002/sim.7951
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发表时间：
2018
影响因子：
2
通讯作者：
Yuanjia Wang
中科院分区：
文献类型：
--
作者：
Ming Sun;Yuanjia Wang
由于缺乏金标准的客观标志物，目前诊断神经系统疾病的做法大多基于临床症状，这些症状可能出现在疾病的晚期。由于患者症状的受试者间和受试者内差异以及临床医生间的差异，临床诊断也存在很大差异。有效地建模疾病过程并利用生物标志物和微妙的临床体征进行早期预测对于提高诊断准确性和设计神经系统疾病的预防性临床试验至关重要且具有挑战性。利用某些生物学特征（即因果基因突变）是疾病机制的一部分以及某些标记物（例如神经影像学测量、运动和认知能力测量）反映病理过程的领域知识，我们提出了一种具有取决于受试者特定特征的随机拐点的非线性模型，以共同估计同一疾病领域中标记物的变化轨迹。该模型根据平均拐点将不同的标记物按时间顺序缩放成可比的进展曲线，并建立标记物的进展与潜在疾病机制之间的关系。该模型还评估了受试者特定特征如何影响不同标记物的动态轨迹，这提供了设计预防性治疗和个性化疾病管理策略的信息。我们进行了广泛的模拟研究，并将我们的方法应用于亨廷顿病的神经影像、认知和运动领域的标记，使用从亨廷顿病的大型多站点自然史研究中收集的数据，我们评估了领域之间疾病损伤的时间顺序。我们发现，某些大脑区域的萎缩首先发生，然后是运动和认知领域，并且表明，普通患者在达到临床诊断年龄时已经经历了严重的区域性脑萎缩。
Due to a lack of a gold standard objective marker, the current practice for diagnosing a neurological disorder is mostly based on clinical symptoms, which may occur in the late stage of the disease. Clinical diagnosis is also subject to high variance due to between‐ and within‐subject variability of patient symptomatology and between‐clinician variability. Effectively modeling disease course and making early prediction using biomarkers and subtle clinical signs are critical and challenging both for improving diagnostic accuracy and designing preventive clinical trials for neurological disorders. Leveraging the domain knowledge that certain biological characteristics (ie, causal genetic mutation) is part of the disease mechanism, and certain markers (eg, neuroimaging measures, motor and cognitive ability measures) reflect pathological process, we propose a nonlinear model with random inflection points depending on subject‐specific characteristics to jointly estimate the changing trajectories of the markers in the same disease domain. The model scales different markers into comparable progression curves with a temporal order based on the mean inflection point and establishes the relationship between the progression of markers with the underlying disease mechanism. The model also assesses how subject‐specific characteristics affect the dynamic trajectory of different markers, which offers information on designing preventive therapeutics and personalized disease management strategy. We perform extensive simulation studies and apply our method to markers in neuroimaging, cognitive, and motor domains of Huntington's disease using the data collected from a large multisite natural history study of Huntington's disease, where we assess the temporal ordering of disease impairment between domains. We show that atrophy from certain brain area occurs first, followed by motor and cognitive domain, and show that an average patient has already experienced substantial regional brain atrophy when reaching clinical diagnosis age.
DOI：
10.1001/jama.1994.03510370056032
发表时间：
1994-04
期刊：
JAMA
影响因子：
--
作者：
Y. Stern;B. Gurland;T. Tatemichi;M. Tang;D. Wilder;R. Mayeux
通讯作者：
Y. Stern;B. Gurland;T. Tatemichi;M. Tang;D. Wilder;R. Mayeux
影响因子：
1.9
作者：
LINDSTROM, MJ;BATES, DM
通讯作者：
BATES, DM